Next, we investigate the distribution of the frequency values, aggregated over the 2017 data set in Fig. REF . We notice that the best Gaussian fit substantially underestimates the tails of the distribution. We quantify these heavy tails by displaying the kurtosis of the grid, which is approximately \(\kappa \approx 4.2\) , compared to the Gaussian \(\kappa ^\text{Gauss}=3\) . This means that large disturbances are much more likely than expected based on a Gaussian assumption.
In particular, the fraction of instances, where \(\Delta f>f_\text{Threshold}\) , increases approximately by a factor of 160.
Therefore, non-Gaussian distributions, for example Lévy-stable [1]} or q-Gaussian [2]}, should rather be considered to model the frequency distribution [3]}.
